Introduction
MoMo-72B-lora-1.8.4-DPO is trained via Direct Preference Optimization(DPO) from MoMo-72B-LoRA-V1.4 as its base model, with several optimizations in hyperparameters.
MoMo-72B-LoRA-V1.4 is trained via Supervised Fine-Tuning (SFT) using LoRA, with the QWEN-72B model as its base-model.
Note that we did not exploit any form of weight merge.
For leaderboard submission, the trained weight is realigned for compatibility with llama.
MoMo-72B is trained using Moreh's MoAI platform, which simplifies the training of large-scale models, and AMD's MI250 GPU.
Details
Used Librarys
- torch
- peft
Used Datasets
- slimorca
- truthy
- orca_dpo_pairs
- No other dataset was used
- No benchmark test set or the training set are used
- data contamination check result
| Model | ARC | MMLU | TruthfulQA | GSM8K |
|---|---|---|---|---|
| V1.4(result < 0.1, %) | TBU | TBU | TBU | TBU |
Used Environments
- AMD MI250 & MoAI platform
- Please visit https://moreh.io/product for more information about MoAI platform
- Or, contact us directly contact@moreh.io
How to use
# pip install transformers==4.35.2
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("moreh/MoMo-72B-lora-1.8.4-DPO")
model = AutoModelForCausalLM.from_pretrained(
"moreh/MoMo-72B-lora-1.8.4-DPO"
)